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141417

Medical Image Compression Using Vector Quantization and Gaussian Mixture Model.

Article

Last updated: 04 Jan 2025

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Subjects

-

Tags

Computer Engineering and Systems

Abstract

Codebook design for vector quantization could be performed using clustering technique. The Gaussian Mixture Modeling (GMM) clustering algorithm involves modeling a statistical distribution by a mixture (or weighted sum) of other distributions. GMM has proven superior efficiency in both time and accuracy and has been used with vector quantization in some applications. This paper introduces a medical image compression technique using GMM clustering algorithm and vector quantization. The parameters of each Gaussian component are estimated using the Expectation Maximization iterative method to minimize the error function (maximize the Likelihood). The results for the proposed compression technique are compared with those obtained using the well-known Kohonen SOM neural network compression technique. 

DOI

10.21608/bfemu.2021.141417

Keywords

Clustering, Vector Quantization, and Image Compression

Authors

First Name

Alaa

Last Name

Elsayad

MiddleName

M.

Affiliation

Computers and Systems Dept., Electronics Research Institute

Email

savad@meit.gov.eg

City

-

Orcid

-

Volume

28

Article Issue

3

Related Issue

20837

Issue Date

2003-09-01

Receive Date

2003-06-19

Publish Date

2021-01-20

Page Start

13

Page End

21

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 28_Issue 3_Pages 13-21.pdf

PDF . 208.5kB

Link

https://bfemu.journals.ekb.eg/article_141417.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=141417

Order

2

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

Medical Image Compression Using Vector Quantization and Gaussian Mixture Model.

Details

Type

Article

Created At

22 Jan 2023